
deeper understanding of user satisfaction and preferences. Examining the relationship between different sentiments
expressed and stated in user reviews and the corresponding ratings would lead to achieving valuable insights into the
factors that influence user engagement and satisfaction. Applicable insights from the resulting factors can
consolidate strategies for enhancing the TikTok app and addressing user concerns.
Regarding competition, with the rapid growth of user-generated content, TikTok faces significant challenges in
content moderation. The company moderates millions of videos uploaded daily. Human moderation alone is
insufficient and costly, especially in mitigating misinformation. Videos presenting claims often require closer
scrutiny than other videos expressing opinions, as they are more likely to breach terms of service. To address this,
we decided to apply text mining and predictive analytics to analyze video transcriptions and develop a
machine-learning model to distinguish between claims and opinions. This model targets to improve TikTok's
moderation process by prioritizing videos for human review based on their content, which would subsequently
increase efficiency and ensure a safer user experience. We aim to create a predictive system that applies techniques
like topic modeling, sentiment analysis, and random forest algorithms. This system will help TikTok streamline
content review, focus on potentially harmful videos, and maintain platform integrity. As TikTok continues as a major
player in the social media landscape, understanding how different video characteristics impact user engagement has
become a norm for content creators and marketers. A method of gaining these insights is segmenting videos utilizing
key features such as text length and video duration. Identifying distinct clusters of videos could lead to uncovering
patterns that could inform improved content strategies.
The final section of cluster analysis targets to segment TikTok videos to reveal unique engagement patterns. We seek
to identify groups of videos with similar characteristics employing clustering techniques. Acquired actionable
insights could lead to optimizing viewer engagement. Understanding these segments would assist content creators in
tailoring and producing videos that resonate with audience preferences, which would enhance the overall
engagement, retention, and satisfaction on the platform. Regarding the analysis, we apply Hierarchical Clustering
and K-means Clustering to categorize videos based on text length and video duration. These methods allow us to
discover natural groupings within the data, which can then be applied to support strategic decisions for content
creation. Further, by examining the characteristics of each cluster, we also expect to develop targeted
recommendations for improving content performance and maximizing audience reach.
Literature Review:
The virality of TikTok videos is a multifaceted phenomenon influenced by numerous factors, including account
verification status, video content categories, and video length. Analyzing the virality of TikTok is critical for
understanding contemporary digital communication, engagement patterns, and how content spreads quickly
throughout public discourse. Analyzing TikTok can further find the broader trends in media consumption, the impact
of digital influencers, and the evolving dynamics of online social interactions. This knowledge would be vital and
applicable for researchers, marketers, and policymakers aiming to engage effectively with digital natives and
leverage the platform's potential for outreach and influence.
Peña-Fernández et al. (2022) indicate that verified accounts on TikTok often enjoy higher trust and engagement due
to perceived authenticity. Verified status can impact user interactions and content reach significantly. However,
Zannettou et al. (2023) found that unverified content may sometimes receive more views because viewers perceive
the unverified account as more genuine and approachable. The dynamics between verification status and virality
deliver a complex interplay where authenticity and relatability play crucial roles in user engagement; driven by
video virality.